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Building of Informatics, Technology and Science
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 26848910     EISSN : 26853310     DOI : -
Core Subject : Science,
Computer Science & IT
Building of Informatics, Technology and Science (BITS) is an open access media in publishing scientific articles that contain the results of research in information technology and computers. Paper that enters this journal will be checked for plagiarism and peer-rewiew first to maintain its quality. This journal is managed by Forum Kerjasama Pendidikan Tinggi (FKPT) published 2 times a year in Juni and Desember. The existence of this journal is expected to develop research and make a real contribution in improving research resources in the field of information technology and computers.
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Articles 82 Documents
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Search results for , issue "Vol 6 No 3 (2024): December 2024" : 82 Documents clear
Modification of the Grey Relational Analysis Method in Determining the Best Mechanic Arshad, Muhammad Waqas; Sulistiani, Heni; Maryana, Sufiatul; Palupiningsih, Pritasari; Rahmanto, Yuri; Setiawansyah, Setiawansyah
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5678

Abstract

Determining the best mechanics in the industry has an important role to ensure the quality and reliability of the products and services offered. Competent and experienced mechanics are able to diagnose and repair accurately and efficiently, thereby minimizing operational downtime and increasing productivity. Without a structured system, mechanical performance appraisals tend to be subjective and inconsistent, which can lead to dissatisfaction among employees and customers. Mechanics may not get clear and constructive feedback on their performance, thus hindering skill development and professionalism. The purpose of the research of the modified Grey Relational Analysis (GRA) using standard deviation is to improve the accuracy and reliability of the decision-making process in situations where the data has a high degree of variability or significant uncertainty. By integrating standard deviations into the GRA, the study aims to account for variations and fluctuations in the data, which allows for more accurate and representative assessment of the criteria. This modification is expected to overcome the weaknesses of traditional GRAs that may not adequately consider data uncertainty, as well as produce more robust and realistic alternative rankings. The results of the best ranking of mechanics, Mechanic FR ranks first with a value of 0.11, followed by Mechanic HS with a value of 0.104. The third place was occupied by Mechanic AY with a score of 0.099.
Modifikasi Metode Simple Additive Weighting Dalam Rekomendasi Restoran Terbaik Berdasarkan Ulasan Pengunjung Prastowo, Kukuh Adi; Sulistiani, Heni; Setiawansyah, Setiawansyah
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5679

Abstract

Simple Additive Weighting (SAW) is a method in DSS that is used to solve multi-criteria problems by adding up the value weights of each alternative. The weakness of SAW is its sensitivity to weight determination and value which can significantly affect the final result. If the weight or value of the criteria is not determined correctly or does not reflect reality well, the results of the decision can be less accurate. The purpose of this study is to modify the SAW method with the name SAW-C to be more effective in providing the best restaurant recommendations based on visitor reviews. SAW modification using a change driven approach not only improves accuracy in decision-making, but also improves adaptability and responsiveness to dynamic and complex environments. The SAW-C method not only improves decision-making accuracy, but also improves adaptability and responsiveness to dynamic and complex environments. SAW-C integrates flexibility and adaptability in managing changes in visitor preferences or the weighting of relevant criteria, which often change over time. With this approach, the recommendation system can dynamically update restaurant ratings based on recent reviews and changing visitor preferences, providing more personalized and relevant recommendations. The results of the ranking of the best restaurants using the SAW-C method show that the results of rank 1 with a final score of 0.92135 are obtained by Flamboyant Restaurant, rank 2 with a final score of 0.70548 obtained by Zozo Garden, and rank 3 with a final score of 0.70312 obtained by Square Restaurant.
Sistem Pendukung Keputusan Pemilihan Aplikasi Jasa Angkut Barang Menggunakan PIPRECIA-S dan Composite Performance Index Sinlae, Alfry Aristo Jansen; Jamaludin, Jamaludin; Nugroho, Nurhasan; Badaruddin, Muliati
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5698

Abstract

Freight service applications play a crucial role in supporting logistics efficiency and goods mobility. However, with the multitude of available applications, users often face difficulties in determining which application best suits their needs. The manual process of selecting these applications requires users to search and compare information from various sources, which demands considerable time and effort and is prone to subjectivity. This study aims to develop a Decision Support System (DSS) that is quick and accurate in determining the best freight service application using the Simplified Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA-S) method and the Composite Performance Index (CPI). The PIPRECIA-S method is used to objectively and systematically determine the criteria weights, while the CPI method is employed to identify the best alternative by integrating various performance aspects into a single, easily understood composite measure. In the conducted case study, the best alternative identified was Lalamove (A2), with a composite index score of 122.55, followed by Deliveree (A3) with a score of 114.39, Lion Parcel (A1) with a score of 109.24, GoBox (A4) with a score of 102.04, and The Lorry (A5) with a score of 99.04. The DSS calculations were consistent with the manual calculations, demonstrating its validity and reliability. Usability testing showed an average score of 90%, indicating that the developed DSS possesses the necessary functionality with an intuitive and user-friendly interface.
Analisis Perbandingan Metode AdaBoost, Gradient Boosting, dan XGBoost Untuk Kalsifikasi Status Gizi Pada Balita Erkamim, Moh.; Tanniewa, Adam M; AP, Irfan; Nurhayati, Nurhayati
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5717

Abstract

Nutritional issues in toddlers are a crucial issue that significantly impacts the health and development of children in Indonesia. Malnutrition can lead to various long-term health problems. Therefore, detecting and classifying the nutritional status of toddlers is very important. This study aims to analyze and compare boosting techniques to classify the nutritional status of toddlers, focusing on three boosting techniques: AdaBoost, Gradient Boosting, and XGBoost. This is done because boosting techniques work by sequentially building models, where each new model attempts to correct the prediction errors of the previous model. The results show that the XGBoost model provides the best performance with a precision of 0.9849, recall of 0.9848, accuracy of 0.9848, F1 score of 0.9848, and ROC-AUC of 0.9994 at an 80:20 data split ratio. Conversely, the AdaBoost model shows the lowest results with a precision of 0.6294, recall of 0.6292, accuracy of 0.6292, F1 score of 0.6291, and ROC-AUC of 0.7581 at a 90:10 data split ratio, caused by its sensitivity to outliers and noise in the data. These findings indicate that XGBoost is the best boosting model for classifying the nutritional status of toddlers, followed by Gradient Boosting, with AdaBoost in the last position. The outstanding performance of XGBoost is due to the use of regularization techniques, effective handling of missing values, and efficient and fast boosting algorithms through parallel processing techniques.
Clustering-Based Stock Return Prediction using K-Medoids and Long Short-Term Memory (LSTM) Sofyan, Denny; Saepudin, Deni
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5744

Abstract

This research focuses on predicting stock returns using the K-Medoids clustering method and the Long Short-Term Memory (LSTM) model. The primary challenge lies in forecasting stock prices, which are then converted into return predictions. Clustering is performed to group stocks with similar price movements, facilitating the preparation of data for training the LSTM model within each cluster. This issue is crucial for aiding investors in making more informed investment decisions by leveraging predictions within specific stock clusters. Through clustering with K-Medoids, based on average returns and return standard deviation, the LSTM model is trained to predict daily returns for each stock within different clusters using the average stock price in each cluster. The data is divided into training (2013-2019) and testing (2020-2022) datasets, with model evaluation conducted using Root Mean Square Error (RMSE). The implementation results indicate prediction performance measured by RMSE for each cluster, with Cluster 3 showing the best performance with a testing RMSE of 0.0300, while Cluster 4 exhibited the worst performance with an RMSE of 0.3995. In the formation of an equal weight portfolio, tested from May 2020 to January 2023, the portfolio value grew from 1 to 2.50, with an average return of 0.0014 and a return standard deviation of 0.0158, indicating potential gains with lower risk compared to the LQ45 index.
Kombinasi Metode Pembobotan Entropy dan MARCOS Dalam Seleksi Penerimaan Karyawan Divisi Keuangan Wahyuni, Dita Septia; Priandika, Adhie Thyo
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5835

Abstract

The selection of employees for the Finance Division is a crucial process to ensure that the selected individuals have the appropriate skills and qualifications to handle complex financial responsibilities. The main problems in the selection of Finance Division employees often revolve around the difficulty in accurately assessing the candidate's technical skills and analytical abilities. The experience and qualifications listed on a resume do not necessarily reflect the candidate's apparent ability to handle complex financial situations or in the face of stringent regulatory challenges. This study aims to apply a combination of entropy and MARCOS weighting methods in the selection of employees of the Finance Division, in order to improve the objectivity and accuracy of the decision-making process. Through this approach, to identify candidates who best suit the company's needs and requirements based on a comprehensive multi-criteria analysis. The combination of Entropy and MARCOS weighting methods in the selection of financial division employees provides a comprehensive and objective approach in decision-making. The Entropy method is used to objectively determine the weight of the criteria based on the degree of uncertainty of the information provided by each criterion, the MARCOS method is used to evaluate and rank candidates based on their proximity to the ideal solution and the distance from the anti-ideal solution. The results of the financial division employee acceptance selection ranking show that Budi Santoso occupies the top position with the highest score of 4.8848. These results provide a clear picture of each candidate's relative position in terms of final assessment, and can serve as a basis for more targeted and objective hiring decisions.
Penerapan Metode K-Nearest Neighbors dan Naïve Bayes pada Analisis Sentimen Pengguna Aplikasi Bstation melalui Platform Playstore Amrillah, Sigit Fathu; Krisbiantoro, Dwi; Prasetyo, Agung
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5863

Abstract

Streaming is a method of distributing digital content directly over the internet, which allows users to access media without the need to download files. Bstation is a streaming platform that combines (OGV) and User-Generated Content (UGC). This research assesses the effectiveness of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in analyzing sentiment in user reviews of the Bstation application, using a data sample of 5,000 reviews. The problem faced is the large number of users of the Bstation application, so sentiment analysis is needed to measure and understand the public's assessment of the application more accurately. This research aims to analyze the sentiment of Bstation users on Playstore and compare the performance of K-Nearest Neighbors (KNN) and Naïve Bayes to determine the best method for classifying reviews and user sentiment patterns. The findings showed that Naïve Bayes achieved 84% accuracy, surpassing KNN which only achieved 68%. Naïve Bayes showed 86% precision and 88% recall for negative sentiment, while achieving 78% precision and 76% recall for positive sentiment. recall for positive sentiment. In contrast, KNN achieved 80% precision and 66% recall for negative sentiments, and 54% recall for positive sentiments. recall for negative sentiments, and 54% precision and 71% recall for positive sentiments. The F1-Score for Naïve Bayes is also higher, reflecting a better balance overall. better balance overall. The macro average and weighted average weighted average for precision, recall, and F1-score with Naïve Bayes were 82% and 83%, respectively, while KNN recorded a macro average of 0.67. In conclusion, Naïve Bayes is more effective in sentiment analysis than KNN, providing more consistent and accurate performance
Comparative Analysis of LSTM, FB Prophet, and Moving Average Methods for Fuel Sales Prediction: A Time Series Forecasting Approach Fadhilah, Ahmad Rizky; Nasution, Arbi Haza; Monika, Winda
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5877

Abstract

Fuel is an important part of vehicles and machinery where sales demand is very high and has various fluctuations. The uncertainty in these fuel sales patterns poses serious problems in inventory management and fuel distribution planning in Indonesia, which can result in excess stock or fuel scarcity in various regions. Additionally, changing trends in vehicle usage and the impact of the COVID-19 pandemic have made accurate sales predictions increasingly difficult. Therefore, this research aims to understand the current and future sales patterns and trends of fuel sales in Indonesia. Careful analysis of prices and other factors such as data processing and other variables is required. This study uses time series analysis methods and compares four models, namely Long Short-Term Memory (LSTM), FB Prophet, Simple Moving Average (SMA), and Exponential Moving Average (EMA). By comparing the results using statistics such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) over various prediction time frames, we assessed the patterns of each model. The results of the analysis show that the LSTM model outperformed all other methods with the lowest MAPE for the prediction of gasoline in the next 31 days, which is 17.11%, while the FB Prophet outperformed all other methods with the lowest MAPE for the prediction of diesel in the next 31 days, which is 18.32%. Although the LSTM model generally outperformed all other algorithms, the FB Prophet model can be used to predict future trends, such as increased use of diesel and decreased use of gasoline which are expected to last within one year. This analysis also provides insights for choosing the right model for a time series problem, including the characteristics of the data to be predicted and analyzed, as well as the assumptions of stationarity and normality of the data. The results of this study indicate that machine learning algorithms can improve the accuracy of time series predictions significantly compared to traditional statistical methods.
Sistem Pakar Diagnosis Penyakit Tanaman Jagung dengan Metode Certainty Factor untuk Meningkatkan Produktivitas Petani Abdilah, Surya; Widyanto, R Arri; Artha, Emilya Ully
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5881

Abstract

Corn is an important commodity in Indonesia after rice and has great potential to be developed as a primary food source. However, corn productivity is often disrupted by pests and diseases. Farmers often have difficulty in identifying and overcoming these problems due to limited knowledge and access to appropriate solutions. This study develops an Expert System for Diagnosing Corn Plant Diseases using the Certainty Factor (CF) method to assist farmers in diagnosing and overcoming pest and disease problems in corn plants. The Certainty Factor method is used to measure the level of expert confidence in the relationship between observed symptoms and the likelihood of disease occurring. This system is designed to provide a diagnosis based on symptom input provided by the user, with a confidence level calculated using a combination of CF values from the inputted symptoms. In testing this system, several cases of corn disease diagnosis were tested using the collected symptom data. The results of system testing on users through the User Acceptance Test (UAT) showed a very good level of acceptance, with a percentage of 94.75%. This system is expected to be an effective tool for corn farmers in increasing their crop productivity in a more efficient and accurate way. Thus, this system has proven to be effective as a tool for farmers to identify and overcome corn plant disease problems more precisely and efficiently.
Classification of Rice Plant Disease Image Using Convolutional Neural Network (CNN) Algorithm based on Amazon Web Service (AWS) Anggraini, Nova; Kusuma, Bagus Adhi; Subarkah, Pungkas; Utomo, Fandy Setyo; Hermanto, Nandang
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5883

Abstract

− In agriculture, rice plays an important role in the Indonesian economy. Rice produces rice, one of the most widely consumed staple food sources in Indonesia. Many factors can cause rice production failure, one of which is leaf pests and diseases. Therefore, early identification and management of plant diseases is an important step in an effort to increase crop yields and ensure food safety. One way to detect rice leaf images early is to perform an image classification process and create a web-based application. The method that has the ability in image processing is deep learning technique with convolutional neural network (CNN) method. The Convolutional Neural Network (CNN) method works to perform and predict diseases in plants by using image categorization or object images. This research aims to apply the web application of image classification of rice plant diseases to the Amazon Web Service (AWS) by identifying and classifying various types of rice leaf diseases using the CNN algorithm, so that farmers can detect rice plant diseases quickly and accurately through image analysis. This application was created using Convolutional Neural Network (CNN) methodology and Software Development Life Cycle (SDLC). The result of this study is that researchers created a web application for the classification of rice plant diseases through leaf images which are divided into 4 categories, namely Healthy, Leaf Blight, Brown Leaf Blight and Hispa, which is made a classification model using CNN with an accuracy value of 0. 8608, then using the streamlit framework to build a website, and utilizing AWS services in the form of Amazon Elastic Compute Cloud (Amazon EC2) as a hosting service, Amazon Simple Storage Service (Amazon S3) as a service for storing rice plant disease classification models and for storing web files, and Amazon Identity and Access Management Role (Amazon IAM) as a service to create a role that gives permission to connect between AWS S3 and AWS EC2. Testing the disease classification model in rice plants implemented on the web in EC2 shows quite good results with an accuracy of 78.5%. This can affect the model's ability to recognize specific disease patterns

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